R: Correlation-Predicted t-Statistic

lait.stat {st} | R Documentation

## Correlation-Predicted t-Statistic

### Description

`lait.stat`, `laicat.fun`, and `lai.tscore` compute the “correlation-predicted” t-statistic of Lai (2008).

### Usage

    lait.stat(X, L, f=0.2, verbose=TRUE)
    lait.fun(L, f=0.2, verbose=TRUE)
    lai.tscore(gene, tscore, corr, f=0.2, plot=FALSE)

### Arguments

`X` | data matrix. Note that the columns correspond to variables (“genes”) and the rows to samples.
`L` | vector with class labels for the two groups.
`verbose` | print out some (more or less useful) information during computation.
`f` | smoother span used in `lowess` (default value: 0.2)
`gene` | the gene for which the Lai t-score is computed
`tscore` | a vector with t-scores
`corr` | a matrix containing correlations
`plot` | show scatter plot correlations versus t-scores with predicted t-score

### Details

The correlation-predicted t-statistic for a gene is the t-score predicted by local linear regression using all other genes. For mathematical details see Lai (2008).

### Value

`lait.stat` returns a vector containing correlation-predicted t-statistic for each variable/gene.

The corresponding `lait.fun` functions return a function that computes the correlation-shared t-statistic when applied to a data matrix (this is very useful for simulations).

The function `lai.tscore` allows to compute the correlation-predicted t-statistic for a gene given a correlation matrix and a vector of t-statistics.

### Author(s)

Verena Zuber and Korbinian Strimmer (http://strimmerlab.org).

### References

Lai, Y.. 2008. Genome-wide co-expression based prediction of differential expression. Bioinformatics 24:666-673.

### See Also

`shrinkcat.stat`, `cst.stat`.

### Examples

    # load st library
    library("st")

    # prostate data set
    data(singh2002)
    X = singh2002$x
    L = singh2002$y

    dim(X)      # 102 6033
    length(L)   # 102

    # compute correlation-predicted t-score for various choices
    # of smoothing span

    ## Not run:

    score1 = lait.stat(X, L, f=0.1)
    idx1 = order(abs(score1), decreasing=TRUE)
    idx1[1:10]
    # 1072  297 1130 4495 4523 4041 1089  955  373 3848

    score3 = lait.stat(X, L, f=0.3)
    idx3 = order(abs(score3), decreasing=TRUE)
    idx3[1:10]
    # 1130  962 1688 1223  583 1118  955  297  698 1219

    score5 = lait.stat(X, L, f=0.5)
    idx5 = order(abs(score5), decreasing=TRUE)
    idx5[1:10]
    #  698  962 1223 1219  739 1172  583  694 3785 3370

    score7 = lait.stat(X, L, f=0.7)
    idx7 = order(abs(score7), decreasing=TRUE)
    idx7[1:10]
    #  698  739 1219  962 3785  725  694  735 3370 1172

    # pick the one with highest correlation to Student t score
    t = studentt.stat(X, L)
    cor(t, score1, method="spearman") # 0.4265832
    cor(t, score3, method="spearman") # 0.471273
    cor(t, score5, method="spearman") # 0.4750564
    cor(t, score7, method="spearman") # 0.4666669

    # focus on gene 19
    t = studentt.stat(X, L)
    R = cor(centroids(X, L, lambda.var=0, centered.data=TRUE,
                  verbose=TRUE)$centered.data)

    lai.tscore(gene=19, t, R, f=0.5, plot=TRUE)

    ## End(Not run)

[Package st version 1.2.5 Index]